← The Digital Executive
The Digital Executive
Ahikam Kaufman on Closing the Financial Data Gap with AI Innovation | Ep 1078
00:12:32
Loading summary
Transcript13 lines
- [00:00]
A
Foreign welcome to Coruscant Technologies, home of the Digital Executive Podcast. Welcome to the Digital Executive. Today's guest is Ahikam Kaufman. Today we have Ahikam Kaufman, co founder and CEO of SafeBooks AI, who is revolutionizing financial data governance with AI driven solutions. With over 20 years in corporate finance, Ahikam has led major firms like Intuit and has successfully guided startups to acquisitions. He'll share insights on fintech trends, fraud prevention and the future of payments. Let's dive into how technology is transforming finance with one of its leading innovators. Well, good afternoon, Ahik, and welcome to the show.
- [00:44]
B
Thank you, Brian. Thank you so much for having me. I'm looking forward to our conversation.
- [00:49]
A
Absolutely, my friend. I appreciate you making the time. You're hailing out of the great state of California there in San Francisco. I'm in Kansas City, so we're a couple hours different difference, but nonetheless, we are going to enjoy our little conversation here today. So, Ahikam, jumping into your first question, you've had a remarkable journey from founding Check Inc. To leading massive computer payments at Intuit. What inspired you to Co found SafeBooks AI? And what key gap in financial data governance are you aiming to solve?
- [01:18]
B
Thank you for the question. I think a lot of us are inspired by problems that we'd like to solve, whether it's in our business life or even personal life or volunteering. After spending many years in the office of the cfo, where I kind of grew up before becoming an entrepreneur, I realized there was like, today there's like a serious gap between the ability of the office on the CFO to deal with the, the amounts of data they have to deal with between or across disparate systems. The reason you need to cope with all that data and be able to monitor it and check it and document it is because that's the nature of finance, right? They need to keep the record straight. They need to close the book. They need to use the data for money purposes, whether it's to get paid or pay. And that makes the whole sensitivity of finance to data being such that they have to get their arms around the data. And the tools today are just not good enough, especially when you have to run transactions across many systems in order to be able to make sure that there's no discrepancies or mistakes. So that kind of challenge inspired me for many years. We solved some of it through internal developments in companies like Intuit. And I thought maybe I can take that experience and share it with more companies as a product and that Kind of was the inspiration.
- [02:43]
A
Thank you, I appreciate that. The thing I took away from what you said there was, you know, know people are inspired by solving problems in the world. You're 100% right. From my vantage point anyway, I fully embrace that we are fulfilled and we find our purpose when we do things in that nature. But I like what you're doing to solve these financial gaps. Right, Ensuring there's no discrepancies or mistakes across a massive managing in this financial industry. These financial transactions and checks so, so important, especially dealing with finances. Ahikam SafeBooks AI focuses on AI powered solutions for financial data governance. Can you walk us through how AI is transforming the way companies handle financial reporting and compliance today?
- [03:25]
B
So I'd like to think that actually to say that in the office of the CFO penetration of AI has been limited. I'd like to think if you look at other parts of business organization like in marketing, in content, in sales, you can see much more AI being involved because the weaknesses of AI today's and the vulnerabilities of AI impose a lesser risk to that specific function. So if you use AI to kind of like empower your fans in go to market organization and the AI make all kind of mistakes, you may obviously unfortunately lose prospects or lose opportunities, but it doesn't create a significant damage to the AI, the office of the CFO because the data is so sensitive, the penetration of AI has been limited. So we are seeing very little penetration of AI today even to some extent. I've even heard customers talking about, please, I want for this solution, I don't want any AI, but I do think that AI can help a lot across the board. We can talk more about that and I think eventually what it can significantly help is by allowing us to automate repetitive tasks and make like connected decisions. But in order to get there, the use cases has to be really. I'd like to think that what we are doing at Facebook is an example of that and I can explain why. So I think AI can definitely change the game in terms of how the office of this CFO can call with data, complex data and the tasks that are associated with it, which are consuming a lot of time today from the organization.
- [05:07]
A
Thank you, appreciate that. And I have noticed that as well. Being in technology, but working in the C suite across the different verticals, we definitely see that the finance or the office of the CFO has been slow to adopt AI. I get it, there's sensitivity of data. We want to make sure that we don't have any additional Breaches or that sort of thing. And part of it is just people are just skeptical, they don't understand AI yet. But I know we're getting there and that's kind of my role is I help people get there and help adopt and be less skeptical. But I took away is the automation of repetitive tasks, decision making, faster reporting, all that's great and that's exactly what you're trying to solve there. So I appreciate that. And Ahikam, with your deep experience in mergers and acquisitions, how do you see AI changing the due diligence process, particularly in areas like financial, financial transparency and operational insights?
- [05:57]
B
I think this is just from my experience. I think having been involved in many m and as I think one of the challenges is how do you go through massive amount of data in order to validate and substantiate the business case or identify the risks associated with whatever target business you're looking to buy. So I'd like to think about the contribution AI from the buyer side, not from the seller side, from the buyer side. I think being able to navigate through mountains of data, whether it's digital data or documented data, contract things like that, and identify topics of discussions or risk could make the diligence process a lot easier. I'd like to think very diligence proposes includes data collection, analysis and then discussion and decision making. I'd like to think that the data collection process, including processing the data is maybe 60, 70% of the time where the analysis of the data and the discussions and decisions are maybe certified. Most of the time engine money and the resources of the FO is concentrated around pulling all the data and processing it. And we think that's like a major area where AI come in and then people can make decisions and the required analysis based on how AI help them to collect data.
- [07:27]
A
Thank you. That's certainly always been a challenge, as you know and you explained it that when you're doing your due diligence, you're taking a big risk. Right, because there are just mountains of data. As you mentioned, there's so much data to validate and substantiate. But you know, as a buyer, if you can leverage AI for that data collection, analysis and discussion, decision making, as you said, that's going to take a lot of the risk out of the whole process. So I appreciate that. Hecam last question of the day. As you look ahead, how do you envision the role of AI evolving in corporate finance over the next five to 10 years and what should finance leaders be doing now to prepare?
- [08:04]
B
I truly think you know, if I may use an analogy, then one of the analogies I'm kind of excited about or kind of like appreciate, it's like look at the car industry or autonomous driving, right? So first you collect a lot of data, you build processing to collect a lot of data. Let's use Tesla, right? And they're using cameras for the most part. And then they process that data. Right. And then they start with limited driving assistance like lane keeping and all of that. Right. But the ultimate goal would be like autonomous driving, right? So the ability to use data. So the way it works is that you create the infrastructure to collect data and then you collect the data and then you use AI, I think, to actually process the data. So you use AI in order to protect our AI. Use AI in the, in the whole processing of being able to process massive amount of data and kind of understand the patterns and all of that and eventually prepare for when you can use all of that data to be able to power model that can actually based on the real time data that the car gets, plus the model can help the car navigate itself. I personally think that autonomous driving will be even better than human beings. You know, we all think the way move cars in the city in San Francisco. I personally think that that driving experience is better than human beings for many, many reasons, including like real time, the city. So back to the office of the cfo. I think obviously we have data, but our ability to connect the data today and what we do is a prerequisite to our AI, which is like being able to clean the data and arrange the data to prepare it for AI and then run AI on top of it should get us to an ultimate goal which is being able to execute a canvas action based on the data. So if I try to kind of like do a fast forwarding, I think a lot of the repetitive tasks which are heavily consuming human resources, like booking the entries and checking the entry, what we are doing, we are actually helping you validate your data and automate your control. And a lot of the activities and the actions that people are naming have, like clothing the books or reconciling the books and all of that. Being able to automate that, automate other decisions, like, you know, checking your payroll would allow the finance team to focus on what they were really trained for, which is making accounting decision, making business decision, supporting the business as opposed to. Right. I think we all much enjoy getting from point A to point B. I know many of us like to drive, but I think many of us don't like to service the car. And some people don't like to drive. But when you own a car and you want to get from point A to point B, you have to take care of the car and you also have to drive. I think being able to focus on like how do you get from point A to point B and then spend the time you need to spend at point B, that's like the same in the final, right? Being able to bridge all the repetitive, heavily manual, to some extent boring and annoying tasks around data and being able to focus on supporting the business, making accounting decisions, looking at the data and making business decisions. That's what really we want finance people to focus on. And the rest is today is necessary even, but tomorrow it's not.
- [11:41]
A
Absolutely. And I love how you're embracing this in the financial vertical. Really. You know, you talked about, you really compared this whole process to autonomous driving cars, right? In order to get there and do this right. You mentioned using AI to prepare for AI. There's many processes to gather these mountains of data, being able to clean, arrange and prepare it with the end goal of automating all the repetitive tasks, leaving that strategic and decision making process to the humans or the financial people. So I really appreciate you breaking that down and sharing your insights into the future. Ahikam, it was certainly a pleasure having you on today and I look forward to speaking with you real soon.
- [12:19]
B
Thank you so much. Brian, thank you so much for having me. I truly enjoyed our conversation.
- [12:23]
A
Bye for now.